Towards a Quality Management System for Flow Modelling: Integrating Uncertainty Analysis in Conceptual Development

Numerical flow models are essential tools in hydraulic engineering and form the basis for a wide range of planning and decision‑making processes. Rapid advances in data availability, modelling techniques, and computational power enable increasingly detailed simulations and appealing visualizations, yet this can also lead to overconfidence in the models and obscure errors, while also complicating the assessment of model robustness. Building on an earlier international survey highlighting the lack of standardized procedures in current practice, the DWA (German Association for Water, Wastewater and Waste) working group WW‑1.7 “Qualitätssicherung und -management beim Einsatz mehrdimensionaler Strömungsmodelle” is developing a structured quality management (QM) system for all participants in the process of flow modeling.

This contribution presents the first conceptual version of this QM system. A guiding design principle is the balance between comprehensive and in-depth quality assurance and the clearly expressed need for intuitive and time‑efficient tools. Requirements and expectations of different stakeholder groups are systematically incorporated into the framework to ensure broad acceptance and usability.

A central component of the development concept is a supporting uncertainty analysis designed to identify critical modelling processes that should be given special consideration in the quality management system. The approach aims to systematically assess how variations in data and key modelling parameters influence model outcomes and contribute to overall uncertainty. To this end, selected modelling processes are examined across several representative test cases. The results are used to refine the prioritization and structuring of QM components by indicating which modelling steps require enhanced quality assurance and documentation.

The development concept presented here provides insight into ongoing efforts toward a comprehensive and robust QM framework, with the aim of enhancing transparency, robustness, and reproducibility in hydraulic flow modelling and reducing the dependence of modelling quality on individual or institutional backgrounds.